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Published July 30, 2013 | Published
Journal Article Open

Reverse-correlating mental representations of sex-typed bodies: the effect of number of trials on image quality

Abstract

Sex categorization is a critical process in social perception. While psychologists have long theorized that perceivers have distinct mental representations of men and women that help them to achieve efficient sex categorizations, researchers have only recently begun using reverse-correlation to visualize the content of these mental representations. The present research addresses two issues concerning this relatively new methodological tool. First, previous studies of reverse-correlation have focused almost exclusively on perceivers' mental representations of faces. Our study demonstrates that this technique can also be used to visualize mental representations of sex-typed bodies. Second, most studies of reverse-correlation have employed a relatively large number of trials (1000+) to capture perceivers' mental representations of a given category. Our study demonstrated that, at least for sex-typed representations of bodies, high quality reverse-correlation images can be obtained with as few as 100 trials. Overall, our findings enhance knowledge of reverse-correlation methodology in general and sex categorization in particular, providing new information for researchers interested in using this technique to understand the complex processes underlying social perception.

Additional Information

© 2013 Lick, Carpinella, Preciado, Spunt and Johnson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Received: 25 January 2013; Accepted: 08 July 2013; Published online: 30 July 2013. This research was supported by National Science Foundation Graduate Research Fellowships (Carpinella, Lick), a Dissertation Year Fellowship (Preciado), and National Science Foundation Grant BCS-1052896 (Johnson). We thank members of the Social Communication Lab for their assistance with data collection, Masumi Iida and Josh Wiley for statistical advice, and Ron Dotsch for sharing his stimulus creation and analysis code.

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September 15, 2023
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